ABONOS ORGÁNICOS EN LA CALIDAD Y MANEJO POSTCOSECHA DE MANGO (Mangifera indica L). VAR. KENT

1 Setup

Instalar version en desarrollo.

if (!require("remotes"))
  install.packages("remotes")
remotes::install_github("flavjack/inti")
source('https://inkaverse.com/setup.r')
library(emmeans)
library(corrplot)
library(multcomp)
library(FSA)

# library(rstatix)
# library(dlookr)
# library(car)


session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Latin America.utf8
 ctype    Spanish_Latin America.utf8
 tz       America/Lima
 date     2024-07-24
 pandoc   3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version  date (UTC) lib source
 agricolae       1.3-7    2023-10-22 [1] CRAN (R 4.4.0)
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 hms             1.1.3    2023-03-21 [1] CRAN (R 4.4.0)
 htmltools       0.5.8.1  2024-04-04 [1] CRAN (R 4.4.0)
 htmlwidgets     1.6.4    2023-12-06 [1] CRAN (R 4.4.0)
 httpuv          1.6.15   2024-03-26 [1] CRAN (R 4.4.0)
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 magrittr        2.0.3    2022-03-30 [1] CRAN (R 4.4.0)
 MASS          * 7.3-60.2 2024-04-26 [2] CRAN (R 4.4.1)
 Matrix          1.7-0    2024-04-26 [2] CRAN (R 4.4.1)
 memoise         2.0.1    2021-11-26 [1] CRAN (R 4.4.0)
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 miniUI          0.1.1.1  2018-05-18 [1] CRAN (R 4.4.0)
 minqa           1.2.7    2024-05-20 [1] CRAN (R 4.4.0)
 mnormt          2.1.1    2022-09-26 [1] CRAN (R 4.4.0)
 multcomp      * 1.4-26   2024-07-18 [1] CRAN (R 4.4.1)
 multcompView    0.1-10   2024-03-08 [1] CRAN (R 4.4.0)
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 Rcpp            1.0.13   2024-07-17 [1] CRAN (R 4.4.1)
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 [1] C:/Users/LENOVO/AppData/Local/R/win-library/4.4
 [2] C:/Program Files/R/R-4.4.1/library

──────────────────────────────────────────────────────────────────────────────

2 Refrencias

  • (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
  • (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
  • (HCPC) https://youtu.be/EJqYTDTJJug

3 Import data

https://docs.google.com/spreadsheets/d/1cjWrS-EVcII85c-l_NuEfTpjhVMI156e8REM9GDVP_w/edit?gid=95386135#gid=95386135

url <- "https://docs.google.com/spreadsheets/d/1cjWrS-EVcII85c-l_NuEfTpjhVMI156e8REM9GDVP_w/edit?gid=95386135#gid=95386135"

gs <- url %>% 
  as_sheets_id()

tratamiento <- gs %>% 
  range_read("tratamientos") %>% 
  rename_with(~ tolower(.))

rendimiento <- gs %>% 
  range_read("rendimiento") %>% 
  rename_with(~ tolower(.)) 

fisio <- gs %>% 
  range_read("fisio") %>% 
  rename_with(~ tolower(.)) %>% 
  merge(., tratamiento) %>% 
  dplyr::select(tratamiento,compost, biol,everything()) %>% 
  merge(., rendimiento) %>% 
  mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>% 
  rename(treat = tratamiento
         , repetition = repeticion
         , composts = compost)

str(fisio)
## 'data.frame':    405 obs. of  15 variables:
##  $ treat     : Factor w/ 9 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ repetition: Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nplantas  : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...
##  $ composts  : Factor w/ 3 levels "0","5","15": 1 1 1 1 1 1 1 1 1 1 ...
##  $ biol      : Factor w/ 3 levels "0","5","10": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nfrutos   : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
##  $ pcfmf     : num  90 70 60 40 70 70 40 60 30 80 ...
##  $ ffmf      : num  13.2 12 10 10.2 12.8 11.6 10.8 11.9 11 11.2 ...
##  $ cifmf     : num  2 2 2 2 2 1.5 2 2 2.5 2.5 ...
##  $ ssfmf     : num  8.8 8.6 8.5 8.2 8.6 9.8 9.7 9.4 9.2 8.5 ...
##  $ phfmf     : num  2.6 2.55 2.52 2.58 2.55 ...
##  $ atfmf     : num  1.39 1.38 1.2 1.12 1.35 1.49 1.29 1.52 1.54 1.42 ...
##  $ msfmf     : num  19.1 19.1 19.1 19.1 19.1 ...
##  $ imf       : num  6.33 6.23 7.08 7.32 6.37 6.58 7.52 6.18 5.97 5.99 ...
##  $ rpp       : num  51.3 51.3 51.3 51.3 51.3 52.4 52.4 52.4 52.4 52.4 ...

consumo <- gs %>% 
  range_read("consumo") %>% 
  rename_with(~ tolower(.)) %>%
  merge(., tratamiento) %>% 
  dplyr::select(tratamiento,compost, biol,everything()) %>% 
  mutate(across(tratamiento:nfrutos, ~ as.factor(.))) %>% 
    rename(treat = tratamiento
         , repetition = repeticion
         , composts = compost
         , n_fruits = nfrutos)

glimpse(consumo)
## Rows: 135
## Columns: 12
## $ treat      <fct> T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0, T0,…
## $ composts   <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 5,…
## $ biol       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ repetition <fct> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1,…
## $ n_fruits   <fct> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5,…
## $ pdfmc      <dbl> 6.68, 6.78, 7.02, 6.78, 6.12, 6.62, 6.91, 7.04, 6.45, 6.50,…
## $ ffmc       <dbl> 3.0, 3.0, 4.0, 3.8, 4.2, 4.0, 3.6, 3.0, 3.0, 3.0, 3.0, 3.4,…
## $ cifmc      <dbl> 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.5, 3.0, 3.0, 3.0, 3.5, 3.0,…
## $ ssfmc      <dbl> 14.6, 15.6, 14.8, 15.0, 14.0, 14.4, 15.5, 15.1, 15.2, 15.2,…
## $ phfmc      <dbl> 4.22, 4.19, 4.21, 4.15, 4.26, 4.34, 4.35, 4.34, 4.30, 4.38,…
## $ atfmc      <dbl> 0.600, 0.700, 0.700, 0.500, 0.500, 0.605, 0.615, 0.625, 0.6…
## $ imf        <dbl> 24.33333, 22.28571, 21.14286, 30.00000, 28.00000, 23.80165,…

4 Data summary

sm <- fisio %>% 
  group_by(treat) %>% 
  summarise(across(pcfmf:rpp, ~ sum(!is.na(.))))

sm
## # A tibble: 9 × 10
##   treat pcfmf  ffmf cifmf ssfmf phfmf atfmf msfmf   imf   rpp
##   <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 T0       45    45    45    45    45    45    45    45    45
## 2 T1       45    45    45    45    45    45    45    45    45
## 3 T2       45    45    45    45    45    45    45    45    45
## 4 T3       45    45    45    45    45    45    45    45    45
## 5 T4       45    45    45    45    45    45    45    45    45
## 6 T5       45    45    45    45    45    45    45    45    45
## 7 T6       45    45    45    45    45    45    45    45    45
## 8 T7       45    45    45    45    45    45    45    45    45
## 9 T8       45    45    45    45    45    45    45    45    45

sm <- consumo %>% 
  group_by(treat) %>% 
  summarise(across(pdfmc:imf, ~ sum(!is.na(.))))

sm
## # A tibble: 9 × 8
##   treat pdfmc  ffmc cifmc ssfmc phfmc atfmc   imf
##   <fct> <int> <int> <int> <int> <int> <int> <int>
## 1 T0       15    15    15    15    15    15    15
## 2 T1       15    15    15    15    15    15    15
## 3 T2       15    15    15    15    15    15    15
## 4 T3       15    15    15    15    15    15    15
## 5 T4       15    15    15    15    15    15    15
## 6 T5       15    15    15    15    15    15    15
## 7 T6       15    15    15    15    15    15    15
## 8 T7       15    15    15    15    15    15    15
## 9 T8       15    15    15    15    15    15    15

5 Objetivos

  • Mostrar el efecto de los abonos orgánicos compost y biol en la calidad del fruto de mango en madurez fisiológica

  • Mostrar el efecto de los abonos orgánicos compost y biol en la calidad del fruto de mango en madurez comercial

5.1 Objetivo Específico 1

Mostrar el efecto de los abonos orgánicos compost y biol en la calidad del fruto de mango en madurez fisiológica

5.1.1 Firmeza de fruto (ffmf)

trait <- "ffmf"
fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       repetition  composts    biol        ffmf        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: ffmf
##                Df  Sum Sq Mean Sq F value             Pr(>F)    
## composts        2  29.954 14.9771 19.6376 0.0000000073854003 ***
## biol            2  48.734 24.3670 31.9495 0.0000000000001369 ***
## composts:biol   4   1.737  0.4344  0.5695             0.6849    
## Residuals     396 302.018  0.7627                               
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
10 0 12.22000 0.1301854 396 11.96406 12.47594 a
5 0 11.80444 0.1301854 396 11.54850 12.06039 ab
0 0 11.38667 0.1301854 396 11.13073 11.64261 b
10 5 12.20444 0.1301854 396 11.94850 12.46039 a
5 5 11.94667 0.1301854 396 11.69073 12.20261 a
0 5 11.49333 0.1301854 396 11.23739 11.74927 b
10 15 12.96000 0.1301854 396 12.70406 13.21594 a
5 15 12.33111 0.1301854 396 12.07517 12.58705 b
0 15 11.95556 0.1301854 396 11.69961 12.21150 b

p1a <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "compost"
           , ylab = "Firmeza del fruto (Kgf)"
           , ylimits = c(0, 16, 4)
           )

p1a

5.2 Color interno del fruto (cifmf)

Excluir esta variable como cuantitativo

trait <- "cifmf"
fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index repetition composts biol cifmf       resi    res_MAD
## 6       6          1        0    0   1.5 -0.6599549 -13.354004
## 9       9          1        0    0   2.5  0.3400451   6.880719
## 10     10          1        0    0   2.5  0.3400451   6.880719
## 11     11          1        0    0   2.5  0.3400451   6.880719
## 18     18          2        0    0   2.5  0.3400451   6.880719
## 21     21          2        0    0   2.5  0.3400451   6.880719
## 23     23          2        0    0   2.5  0.3400451   6.880719
## 24     24          2        0    0   2.5  0.3400451   6.880719
## 25     25          2        0    0   2.5  0.3400451   6.880719
## 26     26          2        0    0   2.5  0.3400451   6.880719
## 27     27          2        0    0   2.5  0.3400451   6.880719
## 28     28          2        0    0   2.5  0.3400451   6.880719
## 34     34          3        0    0   1.5 -0.6800902 -13.761438
## 36     36          3        0    0   3.0  0.8199098  16.590647
## 37     37          3        0    0   2.5  0.3199098   6.473285
## 38     38          3        0    0   2.5  0.3199098   6.473285
## 40     40          3        0    0   3.0  0.8199098  16.590647
## 52     52          1        5    0   2.5  0.3733785   7.555210
## 54     54          1        5    0   2.5  0.3733785   7.555210
## 55     55          1        5    0   2.5  0.3733785   7.555210
## 67     67          2        5    0   1.5 -0.6266215 -12.679513
## 68     68          2        5    0   2.5  0.3733785   7.555210
## 69     69          2        5    0   1.0 -1.1266215 -22.796875
## 70     70          2        5    0   2.5  0.3733785   7.555210
## 72     72          2        5    0   2.5  0.3733785   7.555210
## 76     76          3        5    0   2.5  0.3532431   7.147776
## 81     81          3        5    0   2.5  0.3532431   7.147776
## 83     83          3        5    0   2.5  0.3532431   7.147776
## 84     84          3        5    0   2.5  0.3532431   7.147776
## 85     85          3        5    0   2.5  0.3532431   7.147776
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##                 rawp.BHStud                    adjp                 bholm
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##     out_flag
## 6    OUTLIER
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model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: cifmf
##                Df Sum Sq Mean Sq                       F value
## composts        2 1.5872 0.79358 13798771145988843021802240660
## biol            2 1.6520 0.82599 14362199539619202704044242464
## composts:biol   4 3.4544 0.86360 15016274787197207858626608666
## Residuals     270 0.0000 0.00000                              
##                              Pr(>F)    
## composts      < 0.00000000000000022 ***
## biol          < 0.00000000000000022 ***
## composts:biol < 0.00000000000000022 ***
## Residuals                              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
1 5 0 2.0 0 270 2.0 2.0 a
3 10 0 2.0 0 270 2.0 2.0 a
2 0 0 2.0 0 270 2.0 2.0 a
4 10 5 2.0 0 270 2.0 2.0 a
5 5 5 2.0 0 270 2.0 2.0 a
6 0 5 2.0 0 270 2.0 2.0 a
7 10 15 2.5 0 270 2.5 2.5 a
8 5 15 2.0 0 270 2.0 2.0 b
9 0 15 2.0 0 270 2.0 2.0 b

p1b <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Color interno del fruto"
           , ylimits = c(0, 3, 1)
           )

p1b

5.3 Potencia de hidrogeno del fruto (phfmf)

trait <- "phfmf"
fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index repetition composts biol    phfmf      resi res_MAD  rawp.BHStud
## 390   390          2       15   10 3.507795 0.7353291 3.98034 0.0000688167
##             adjp      bholm out_flag
## 390 0.0000688167 0.02787076  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: phfmf
##                Df  Sum Sq Mean Sq F value        Pr(>F)    
## composts        2  0.8058 0.40290 12.1492 0.00000757832 ***
## biol            2  1.2013 0.60065 18.1126 0.00000002978 ***
## composts:biol   4  0.5173 0.12932  3.8996      0.004051 ** 
## Residuals     395 13.0991 0.03316                          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
2 10 0 2.711098 0.0271466 395 2.657728 2.764468 a
1 0 0 2.594716 0.0271466 395 2.541346 2.648086 b
3 5 0 2.526739 0.0271466 395 2.473369 2.580109 b
4 10 5 2.737604 0.0271466 395 2.684234 2.790974 a
5 5 5 2.634018 0.0271466 395 2.580648 2.687388 b
6 0 5 2.540791 0.0271466 395 2.487421 2.594161 c
7 10 15 2.745579 0.0274534 395 2.691606 2.799552 a
8 5 15 2.710735 0.0271466 395 2.657365 2.764104 a
9 0 15 2.692818 0.0271466 395 2.639448 2.746188 a

p1c <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "pH del Fruto"
           , ylimits = c(0, 4, 1)
           )

p1c

5.4 Solidos solubles del fruto (ssfmf)

trait <- "ssfmf"
fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index repetition composts biol ssfmf     resi  res_MAD    rawp.BHStud
## 390   390          2       15   10  12.1 2.787154 4.673809 0.000002956642
##               adjp      bholm out_flag
## 390 0.000002956642 0.00119744  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: ssfmf
##                Df  Sum Sq Mean Sq F value         Pr(>F)    
## composts        2  10.124  5.0620 13.9979 0.000001338318 ***
## biol            2  14.392  7.1959 19.8987 0.000000005838 ***
## composts:biol   4   4.753  1.1882  3.2858        0.01146 *  
## Residuals     395 142.842  0.3616                           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
1 10 0 9.194444 0.0896445 395 9.018204 9.370684 a
2 5 0 8.767778 0.0896445 395 8.591538 8.944018 b
3 0 0 8.574444 0.0896445 395 8.398205 8.750684 b
4 10 5 9.291111 0.0896445 395 9.114871 9.467351 a
5 5 5 8.913333 0.0896445 395 8.737093 9.089573 b
6 0 5 8.646667 0.0896445 395 8.470427 8.822907 b
7 5 15 9.293333 0.0896445 395 9.117093 9.469573 a
9 10 15 9.244318 0.0906575 395 9.066087 9.422550 a
8 0 15 9.127778 0.0896445 395 8.951538 9.304018 a

p1d <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Solidos solubles del fruto (brix^{o})"
           , ylimits = c(0, 12, 2)
           )

p1d

5.5 Acidez titulable del fruto (atfmf)

trait <- "atfmf"
fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       repetition  composts    biol        atfmf       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: atfmf
##                Df  Sum Sq  Mean Sq F value        Pr(>F)    
## composts        2 0.19915 0.099574 15.5979 0.00000030165 ***
## biol            2 0.23570 0.117851 18.4609 0.00000002161 ***
## composts:biol   4 0.08017 0.020043  3.1397       0.01465 *  
## Residuals     396 2.52799 0.006384                          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
3 0 0 1.365333 0.0119106 396 1.341917 1.388749 a
2 5 0 1.313556 0.0119106 396 1.290140 1.336971 b
1 10 0 1.260444 0.0119106 396 1.237029 1.283860 c
6 5 5 1.301556 0.0119106 396 1.278140 1.324971 a
4 0 5 1.298000 0.0119106 396 1.274584 1.321416 a
5 10 5 1.270889 0.0119106 396 1.247473 1.294305 a
9 0 15 1.278000 0.0119106 396 1.254584 1.301416 a
8 5 15 1.263889 0.0119106 396 1.240473 1.287305 ab
7 10 15 1.235111 0.0119106 396 1.211695 1.258527 b

p1e <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "compost"
           , ylab = "Acidez titulable del fruto ('%')"
           , ylimits = c(0, 2, 1)
           )

p1e

5.6 Materia seca del fruto (msfmf)

trait <- "msfmf"
fb <- fisio

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       repetition  composts    biol        msfmf       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: msfmf
##                Df  Sum Sq Mean Sq F value                Pr(>F)    
## composts        2  45.336  22.668 30.4208    0.0000000000005129 ***
## biol            2  72.171  36.085 48.4270 < 0.00000000000000022 ***
## composts:biol   4   6.782   1.695  2.2753               0.06058 .  
## Residuals     396 295.079   0.745                                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
10 0 20.02778 0.1286813 396 19.77479 20.28076 a
5 0 19.31778 0.1286813 396 19.06479 19.57076 b
0 0 19.26889 0.1286813 396 19.01590 19.52187 b
10 5 20.00222 0.1286813 396 19.74924 20.25521 a
5 5 19.55744 0.1286813 396 19.30446 19.81043 b
0 5 19.07333 0.1286813 396 18.82035 19.32632 c
10 15 20.96133 0.1286813 396 20.70835 21.21432 a
5 15 20.21600 0.1286813 396 19.96302 20.46898 b
0 15 19.57556 0.1286813 396 19.32257 19.82854 c

p1f <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "compost"
           , ylab = "Materia seca del fruto ('%')"
           , ylimits = c(0, 25, 5)
           )

p1f

5.7 Figure 1

legend <- cowplot::get_plot_component(p1a, 'guide-box-top', return_all = TRUE)

p1 <- list(p1a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1e + theme(legend.position="none")
           , p1f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1)) %>% 
  ggsave2(plot = ., "files/Fig-1.jpg"
         , units = "cm"
         , width = 25
         , height = 22
         )

knitr::include_graphics("files/Fig-1.jpg")

5.8 Multivariate

mv <- fisio %>% 
  group_by(composts, biol) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", composts:biol, sep = "-") %>% 
  rename(Treat = treat)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7  Dim.8
## Variance               7.14   0.92   0.52   0.30   0.10   0.02   0.01   0.00
## % of var.             79.29  10.27   5.74   3.28   1.11   0.21   0.10   0.01
## Cumulative % of var.  79.29  89.56  95.30  98.58  99.69  99.89  99.99 100.00
## 
## Individuals
##          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## 1     |  4.39 | -4.14 26.70  0.89 |  0.91  9.94  0.04 | -0.02  0.01  0.00 |
## 2     |  2.85 | -2.46  9.45  0.75 |  0.60  4.36  0.04 | -0.94 18.96  0.11 |
## 3     |  1.94 |  1.03  1.66  0.28 | -0.67  5.42  0.12 | -1.41 42.81  0.53 |
## 4     |  3.09 | -2.82 12.40  0.83 |  0.06  0.04  0.00 |  0.87 16.47  0.08 |
## 5     |  0.98 | -0.79  0.98  0.66 | -0.20  0.47  0.04 |  0.32  2.18  0.11 |
## 6     |  2.04 |  1.64  4.19  0.65 | -1.13 15.46  0.31 | -0.20  0.89  0.01 |
## 7     |  1.35 |  0.50  0.38  0.14 | -0.88  9.25  0.42 |  0.85 15.42  0.39 |
## 8     |  2.36 |  2.19  7.47  0.86 | -0.71  6.07  0.09 |  0.34  2.43  0.02 |
## 9     |  5.27 |  4.86 36.77  0.85 |  2.02 48.98  0.15 |  0.20  0.83  0.00 |
## 
## Variables
##         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## pcfmf |  0.96 12.91  0.92 | -0.04  0.18  0.00 | -0.11  2.41  0.01 |
## ffmf  |  0.97 13.27  0.95 |  0.17  3.23  0.03 | -0.12  2.91  0.02 |
## cifmf |  0.57  4.59  0.33 |  0.81 71.60  0.66 |  0.07  0.95  0.00 |
## ssfmf |  0.95 12.55  0.90 | -0.29  9.06  0.08 | -0.10  1.89  0.01 |
## phfmf |  0.90 11.35  0.81 | -0.17  3.25  0.03 | -0.02  0.06  0.00 |
## atfmf | -0.93 12.00  0.86 |  0.13  1.88  0.02 |  0.01  0.03  0.00 |
## msfmf |  0.95 12.62  0.90 |  0.23  5.63  0.05 | -0.12  2.84  0.01 |
## imf   |  0.97 13.20  0.94 | -0.20  4.29  0.04 | -0.07  1.05  0.01 |
## rpp   |  0.73  7.51  0.54 | -0.09  0.88  0.01 |  0.67 87.86  0.45 |
## 
## Supplementary categories
##          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3  cos2 v.test  
## 0-0   |  4.39 | -4.14  0.89  -1.55 |  0.91  0.04   0.95 | -0.02  0.00  -0.03 |
## 0-10  |  1.94 |  1.03  0.28   0.39 | -0.67  0.12  -0.70 | -1.41  0.53  -1.96 |
## 0-5   |  2.85 | -2.46  0.75  -0.92 |  0.60  0.04   0.63 | -0.94  0.11  -1.31 |
## 15-0  |  1.35 |  0.50  0.14   0.19 | -0.88  0.42  -0.91 |  0.85  0.39   1.18 |
## 15-10 |  5.27 |  4.86  0.85   1.82 |  2.02  0.15   2.10 |  0.20  0.00   0.27 |
## 15-5  |  2.36 |  2.19  0.86   0.82 | -0.71  0.09  -0.74 |  0.34  0.02   0.47 |
## 5-0   |  3.09 | -2.82  0.83  -1.06 |  0.06  0.00   0.06 |  0.87  0.08   1.22 |
## 5-10  |  2.04 |  1.64  0.65   0.61 | -1.13  0.31  -1.18 | -0.20  0.01  -0.28 |
## 5-5   |  0.98 | -0.79  0.66  -0.30 | -0.20  0.04  -0.21 |  0.32  0.11   0.44 |


f2a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                )

f2b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                , ylim = c(-3,3)
                ) 

5.9 Figure 2

list(f2a, f2b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1, 1.5)
            ) %>% 
  ggsave2(plot = ., "files/Fig-2.jpg", units = "cm"
         , width = 25
         , height = 10
         )

knitr::include_graphics("files/Fig-2.jpg")

6 Correlation

cor <- mv %>% 
  dplyr::select(where(is.numeric)) %>% 
  cor(., method ="pearson")

cor %>% kable()
pcfmf ffmf cifmf ssfmf phfmf atfmf msfmf imf rpp
pcfmf 1.0000000 0.9608678 0.4994040 0.9307446 0.8014322 -0.9032846 0.8923145 0.9432362 0.6402353
ffmf 0.9608678 1.0000000 0.6839396 0.8811729 0.8263723 -0.8804054 0.9730600 0.9158768 0.6176584
cifmf 0.4994040 0.6839396 1.0000000 0.2991542 0.3781309 -0.4311611 0.7140435 0.3932697 0.3897352
ssfmf 0.9307446 0.8811729 0.2991542 1.0000000 0.9232590 -0.8857042 0.8519632 0.9763968 0.6542042
phfmf 0.8014322 0.8263723 0.3781309 0.9232590 1.0000000 -0.7551839 0.8659197 0.8841685 0.6578585
atfmf -0.9032846 -0.8804054 -0.4311611 -0.8857042 -0.7551839 1.0000000 -0.8043641 -0.9630024 -0.6729750
msfmf 0.8923145 0.9730600 0.7140435 0.8519632 0.8659197 -0.8043641 1.0000000 0.8719723 0.5948866
imf 0.9432362 0.9158768 0.3932697 0.9763968 0.8841685 -0.9630024 0.8719723 1.0000000 0.6752525
rpp 0.6402353 0.6176584 0.3897352 0.6542042 0.6578585 -0.6729750 0.5948866 0.6752525 1.0000000

sf1 <- ~ {
  
  corrplot::corrplot(cor, method = "number", type = "upper")
}

list(sf1) %>% 
  plot_grid(plotlist = .) %>% 
  ggsave2(plot = ., "files/FigS1.jpg", units = "cm"
         , width = 15, height = 15)

knitr::include_graphics("files/FigS1.jpg")

6.0.1 Objetivo Específico 2

Mostrar el efecto de los abonos orgánicos compost y biol en la calidad del fruto de mango en madurez comercial

7 Porcentaje deshidratación del fruto (pdfmc)

trait <- "pdfmc"
cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       repetition  composts    biol        pdfmc       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: pdfmc
##                Df Sum Sq Mean Sq F value           Pr(>F)    
## composts        2 2.9454 1.47269 28.9546 0.00000000004501 ***
## biol            2 1.6263 0.81316 15.9875 0.00000064906170 ***
## composts:biol   4 0.2066 0.05164  1.0153           0.4022    
## Residuals     126 6.4086 0.05086                             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
3 0 0 6.760667 0.0582307 126 6.645430 6.875903 a
2 5 0 6.539333 0.0582307 126 6.424097 6.654570 b
1 10 0 6.408000 0.0582307 126 6.292763 6.523237 b
6 0 5 6.685333 0.0582307 126 6.570097 6.800570 a
5 5 5 6.492000 0.0582307 126 6.376763 6.607237 ab
4 10 5 6.375333 0.0582307 126 6.260097 6.490570 b
9 0 15 6.301333 0.0582307 126 6.186096 6.416570 a
8 5 15 6.236000 0.0582307 126 6.120763 6.351237 a
7 10 15 6.162667 0.0582307 126 6.047430 6.277903 a

p2a <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Deshidratación del fruto ('%')"
           , ylimits = c(0, 8, 2)
           )

p2a

8 Solidos solubles del fruto (ssfmc)

trait <- "ssfmc"
cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       repetition  composts    biol        ssfmc       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: ssfmc
##                Df Sum Sq Mean Sq F value        Pr(>F)    
## composts        2 13.192  6.5961 19.7094 0.00000003569 ***
## biol            2  6.876  3.4379 10.2725 0.00007365188 ***
## composts:biol   4  0.332  0.0830  0.2479        0.9105    
## Residuals     126 42.168  0.3347                          
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
10 0 15.44000 0.149369 126 15.14440 15.73560 a
5 0 15.04667 0.149369 126 14.75107 15.34226 ab
0 0 14.92667 0.149369 126 14.63107 15.22226 b
10 5 15.46667 0.149369 126 15.17107 15.76226 a
5 5 15.22000 0.149369 126 14.92440 15.51560 ab
0 5 14.84667 0.149369 126 14.55107 15.14226 b
10 15 16.12000 0.149369 126 15.82440 16.41560 a
5 15 15.72667 0.149369 126 15.43107 16.02226 ab
0 15 15.61333 0.149369 126 15.31774 15.90893 b

p2b <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Solidos solubles del fruto (brix^{o})"
           , ylimits = c(0, 18, 3)
           )

p2b

9 Acidez titulable del fruto (atfmc)

trait <- "atfmc"
cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##    index repetition composts biol atfmc       resi   res_MAD   rawp.BHStud
## 4      4          1        0    0  0.50 -0.1157152 -4.533406 0.00000580400
## 5      5          1        0    0  0.50 -0.1157152 -4.533406 0.00000580400
## 18    18          1        5    0  0.74  0.1062848  4.163951 0.00003127878
## 27    27          3        5    0  0.50 -0.1006342 -3.942576 0.00008061124
##             adjp        bholm out_flag
## 4  0.00000580400 0.0007835399  OUTLIER
## 5  0.00000580400 0.0007835399  OUTLIER
## 18 0.00003127878 0.0041600772  OUTLIER
## 27 0.00008061124 0.0106406836  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: atfmc
##                Df   Sum Sq  Mean Sq F value                Pr(>F)    
## composts        2 0.219750 0.109875 94.7899 < 0.00000000000000022 ***
## biol            2 0.054531 0.027266 23.5222         0.00000000229 ***
## composts:biol   4 0.023460 0.005865  5.0598             0.0008311 ***
## Residuals     122 0.141416 0.001159                                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
2 0 0 0.6169231 0.0094427 122 0.5982303 0.6356159 a
3 10 0 0.5946667 0.0087907 122 0.5772646 0.6120687 a
1 5 0 0.5940000 0.0087907 122 0.5765979 0.6114021 a
6 0 5 0.6192308 0.0094427 122 0.6005380 0.6379236 a
5 5 5 0.5496667 0.0087907 122 0.5322646 0.5670687 b
4 10 5 0.5346667 0.0087907 122 0.5172646 0.5520687 b
9 0 15 0.5193333 0.0087907 122 0.5019313 0.5367354 a
8 5 15 0.5138667 0.0087907 122 0.4964646 0.5312687 a
7 10 15 0.4746667 0.0087907 122 0.4572646 0.4920687 b

p2c <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Acidez titulable del fruto ('%')"
           , ylimits = c(0, 0.8, 0.2)
           )

p2c

10 Potencial de hidrógeno del fruto (phfmc)

trait <- "phfmc"
cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##    index repetition composts biol phfmc       resi   res_MAD   rawp.BHStud
## 27    27          3        5    0   3.8 -0.4029042 -4.421673 0.00000979398
##             adjp       bholm out_flag
## 27 0.00000979398 0.001322187  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: phfmc
##                Df  Sum Sq Mean Sq F value                Pr(>F)    
## composts        2 1.51062 0.75531 54.8131 < 0.00000000000000022 ***
## biol            2 0.64152 0.32076 23.2776         0.00000000255 ***
## composts:biol   4 0.26493 0.06623  4.8065              0.001221 ** 
## Residuals     125 1.72246 0.01378                                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
10 0 4.388000 0.0303092 125 4.328014 4.447986 a
5 0 4.369333 0.0303092 125 4.309348 4.429319 a
0 0 4.332000 0.0303092 125 4.272014 4.391986 a
10 5 4.484000 0.0303092 125 4.424014 4.543986 a
5 5 4.429333 0.0303092 125 4.369348 4.489319 a
0 5 4.201429 0.0313730 125 4.139337 4.263520 b
10 15 4.692667 0.0303092 125 4.632681 4.752652 a
5 15 4.568667 0.0303092 125 4.508681 4.628652 b
0 15 4.520000 0.0303092 125 4.460014 4.579986 b

p2d <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "pH del Fruto"
           , ylimits = c(0, 6, 2)
           )

p2d

11 Color interno del fruto (cifmc)

trait <- "cifmc"
cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       repetition  composts    biol        cifmc       resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: cifmc
##                Df  Sum Sq Mean Sq F value   Pr(>F)   
## composts        2  1.4890 0.74452  5.4384 0.005427 **
## biol            2  1.9690 0.98452  7.1915 0.001103 **
## composts:biol   4  2.0914 0.52285  3.8192 0.005776 **
## Residuals     126 17.2493 0.13690                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
10 0 3.766667 0.0955334 126 3.577609 3.955725 a
5 0 3.566667 0.0955334 126 3.377609 3.755724 a
0 0 3.066667 0.0955334 126 2.877609 3.255724 b
10 5 3.633333 0.0955334 126 3.444275 3.822391 a
5 5 3.633333 0.0955334 126 3.444275 3.822391 a
0 5 3.593333 0.0955334 126 3.404275 3.782391 a
10 15 3.800000 0.0955334 126 3.610942 3.989058 a
5 15 3.700000 0.0955334 126 3.510942 3.889058 a
0 15 3.666667 0.0955334 126 3.477609 3.855724 a

p2e <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "compost"
           , ylab = "Color interno del fruto"
           , ylimits = c(0, 5, 1)
           )

p2e

12 Firmeza del fruto (ffmc)

trait <- "ffmc"
cs <- consumo

lmm <- paste({{trait}}, "~ 1 + (1|repetition) + composts*biol") %>% as.formula()

lmd <- paste({{trait}}, "~ composts*biol") %>% as.formula()

rmout <- cs %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       repetition  composts    biol        ffmc        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: ffmc
##                Df  Sum Sq Mean Sq F value         Pr(>F)    
## composts        2  6.6441  3.3221 25.4488 0.000000000521 ***
## biol            2  2.7739  1.3870 10.6248 0.000054440296 ***
## composts:biol   4  1.4536  0.3634  2.7839        0.02947 *  
## Residuals     126 16.4480  0.1305                           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ biol|composts) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
biol composts emmean SE df lower.CL upper.CL group
2 10 0 4.053333 0.093288 126 3.868719 4.237947 a
1 0 0 3.546667 0.093288 126 3.362052 3.731281 b
3 5 0 3.440000 0.093288 126 3.255386 3.624614 b
4 10 5 4.093333 0.093288 126 3.908719 4.277947 a
5 5 5 4.040000 0.093288 126 3.855386 4.224614 a
6 0 5 3.813333 0.093288 126 3.628719 3.997947 a
7 10 15 4.333333 0.093288 126 4.148719 4.517948 a
8 5 15 4.213333 0.093288 126 4.028719 4.397947 a
9 0 15 4.120000 0.093288 126 3.935386 4.304614 a

p2f <- mc %>% 
  plot_smr(x = "composts"
           , y = "emmean"
           , group = "biol"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "compost"
           , ylab = "Firmeza del fruto (Kgf)"
           , ylimits = c(0, 6, 2)
           )

p2f

12.1 Figure 3

legend <- cowplot::get_plot_component(p2a, 'guide-box-top', return_all = TRUE)

p2 <- list(p2a + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2d + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p2e + theme(legend.position="none")
           , p2f + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            ) 

plot_grid(legend, p2, ncol = 1, align = 'v', rel_heights = c(0.05, 1)) %>% 
  ggsave2(plot = ., "files/Fig-3.jpg"
         , units = "cm"
         , width = 26
         , height = 24
         )

knitr::include_graphics("files/Fig-3.jpg")

12.2 Multivariate

mv <- consumo %>% 
  group_by(composts, biol) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", composts:biol, sep = "-") %>% 
  rename(Treat = treat)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7
## Variance               5.94   0.72   0.22   0.08   0.03   0.01   0.00
## % of var.             84.79  10.35   3.12   1.10   0.46   0.18   0.01
## Cumulative % of var.  84.79  95.14  98.26  99.36  99.81  99.99 100.00
## 
## Individuals
##          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## 1     |  3.99 | -3.53 23.36  0.78 | -1.82 50.71  0.21 |  0.30  4.70  0.01 |
## 2     |  2.48 | -2.15  8.62  0.75 |  0.02  0.01  0.00 | -1.22 76.40  0.24 |
## 3     |  1.45 | -0.25  0.11  0.03 |  1.28 25.17  0.78 | -0.03  0.05  0.00 |
## 4     |  3.26 | -3.01 16.91  0.85 |  1.12 19.22  0.12 |  0.47 11.33  0.02 |
## 5     |  0.61 | -0.15  0.04  0.06 |  0.20  0.60  0.10 |  0.33  5.43  0.28 |
## 6     |  0.89 |  0.85  1.36  0.92 | -0.05  0.04  0.00 |  0.18  1.59  0.04 |
## 7     |  1.64 |  1.62  4.92  0.98 | -0.15  0.35  0.01 |  0.02  0.03  0.00 |
## 8     |  2.31 |  2.29  9.85  0.99 | -0.11  0.19  0.00 |  0.04  0.08  0.00 |
## 9     |  4.36 |  4.31 34.81  0.98 | -0.49  3.71  0.01 | -0.09  0.39  0.00 |
## 
## Variables
##         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## pdfmc | -0.98 16.06  0.95 | -0.11  1.57  0.01 |  0.14  9.00  0.02 |
## ffmc  |  0.89 13.39  0.79 |  0.22  6.42  0.05 |  0.39 71.20  0.16 |
## cifmc |  0.71  8.50  0.50 |  0.68 64.34  0.47 | -0.15  9.80  0.02 |
## ssfmc |  0.98 16.24  0.96 | -0.06  0.49  0.00 | -0.04  0.73  0.00 |
## phfmc |  0.94 14.82  0.88 | -0.30 12.42  0.09 | -0.14  8.48  0.02 |
## atfmc | -0.95 15.29  0.91 |  0.25  8.69  0.06 | -0.03  0.35  0.00 |
## imf   |  0.97 15.70  0.93 | -0.21  6.07  0.04 |  0.03  0.44  0.00 |
## 
## Supplementary categories
##          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3  cos2 v.test  
## 0-0   |  3.99 | -3.53  0.78  -1.45 | -1.82  0.21  -2.14 |  0.30  0.01   0.65 |
## 0-10  |  1.45 | -0.25  0.03  -0.10 |  1.28  0.78   1.51 | -0.03  0.00  -0.07 |
## 0-5   |  2.48 | -2.15  0.75  -0.88 |  0.02  0.00   0.03 | -1.22  0.24  -2.62 |
## 15-0  |  1.64 |  1.62  0.98   0.67 | -0.15  0.01  -0.18 |  0.02  0.00   0.05 |
## 15-10 |  4.36 |  4.31  0.98   1.77 | -0.49  0.01  -0.58 | -0.09  0.00  -0.19 |
## 15-5  |  2.31 |  2.29  0.99   0.94 | -0.11  0.00  -0.13 |  0.04  0.00   0.08 |
## 5-0   |  3.26 | -3.01  0.85  -1.23 |  1.12  0.12   1.32 |  0.47  0.02   1.01 |
## 5-10  |  0.89 |  0.85  0.92   0.35 | -0.05  0.00  -0.06 |  0.18  0.04   0.38 |
## 5-5   |  0.61 | -0.15  0.06  -0.06 |  0.20  0.10   0.23 |  0.33  0.28   0.70 |


f4a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                )

f4b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                , ylim = c(-3,3)
                ) 

12.3 Figure 4

list(f4a, f4b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1, 1.5)
            ) %>% 
  ggsave2(plot = ., "files/Fig-4.jpg", units = "cm"
         , width = 25
         , height = 10
         )

knitr::include_graphics("files/Fig-4.jpg")

13 Correlation

cor <- mv %>% 
  dplyr::select(where(is.numeric)) %>% 
  cor(., method ="pearson")

cor %>% kable()
pdfmc ffmc cifmc ssfmc phfmc atfmc imf
pdfmc 1.0000000 -0.8429407 -0.7772793 -0.9624332 -0.9051190 0.8929691 -0.9012038
ffmc -0.8429407 1.0000000 0.7184836 0.8508419 0.7231591 -0.7997702 0.8182849
cifmc -0.7772793 0.7184836 1.0000000 0.6516900 0.4781254 -0.5128373 0.5481724
ssfmc -0.9624332 0.8508419 0.6516900 1.0000000 0.9468725 -0.9204049 0.9520934
phfmc -0.9051190 0.7231591 0.4781254 0.9468725 1.0000000 -0.9583658 0.9515053
atfmc 0.8929691 -0.7997702 -0.5128373 -0.9204049 -0.9583658 1.0000000 -0.9846427
imf -0.9012038 0.8182849 0.5481724 0.9520934 0.9515053 -0.9846427 1.0000000

sf2 <- ~ {
  
  corrplot::corrplot(cor, method = "number", type = "upper")
}

list(sf2) %>% 
  plot_grid(plotlist = .) %>% 
  ggsave2(plot = ., "files/FigS2.jpg", units = "cm"
         , width = 15, height = 15)

knitr::include_graphics("files/FigS2.jpg")